# ONNX Runtime Java API
The ONNX runtime provides a Java binding for running inference on ONNX models on a JVM, using Java 8 or newer.

Two jar files are created during the build process, one contains the onnxruntime shared library, the JNI binding and the Java class files, and the other only contains the class files. By default the shared libraries are loaded from the classpath in a folder called `/lib`, if you wish to have them load from `java.library.path` then supply `-DORT_LOAD_FROM_LIBRARY_PATH` to the JVM at runtime.

## Sample Code

The unit tests contain several examples of loading models, inspecting input/output node shapes and types, as well as constructing tensors for scoring. 

* [../java/src/test/java/ai/onnxruntime/InferenceTest.java#L66](../java/src/test/java/ai/onnxruntime/InferenceTest.java#L66)

## Getting Started
Here is simple tutorial for getting started with running inference on an existing ONNX model for a given input data. The model is typically trained using any of the well-known training frameworks and exported into the ONNX format. 
Note the code presented below uses syntax available from Java 10 onwards. The Java 8 syntax is similar but more verbose.
To start a scoring session, first create the `OrtEnvironment`, then open a session using the `OrtSession` class, passing in the file path to the model as a parameter.
    
    var env = OrtEnvironment.getEnvironment();
    var session = env.createSession("model.onnx",new OrtSession.SessionOptions());

Once a session is created, you can execute queries using the `run` method of the `OrtSession` object. 
At the moment we support `OnnxTensor` inputs, and models can produce `OnnxTensor`, `OnnxSequence` or `OnnxMap` outputs. The latter two are more likely when scoring models produced by frameworks like scikit-learn.
The run call expects a `Map<String,OnnxTensor>` where the keys match input node names stored in the model. These can be viewed by calling `session.getInputNames()` or `session.getInputInfo()` on an instantiated session.
The run call produces a `Result` object, which contains a `Map<String,OnnxValue>` representing the output. The `Result` object is `AutoCloseable` and can be used in a try-with-resources statement to 
prevent references from leaking out. Once the `Result` object is closed, all it's child `OnnxValue`s are closed too.
    
    OnnxTensor t1,t2;
    var inputs = Map.of("name1",t1,"name2",t2);
    try (var results = session.run(inputs)) {
        // manipulate the results
    }

You can load your input data into OnnxTensor objects in several ways. The most efficient way is to use a `java.nio.Buffer`, but it's possible to use multidimensional arrays too. If constructed using arrays the arrays must not be ragged.

    FloatBuffer sourceData;  // assume your data is loaded into a FloatBuffer
    long[] dimensions;       // and the dimensions of the input are stored here
    var tensorFromBuffer = OnnxTensor.createTensor(env,sourceData,dimensions);

    float[][] sourceArray = new float[28][28];  // assume your data is loaded into a float array 
    var tensorFromArray = OnnxTensor.createTensor(env,sourceArray);

Here is a [complete sample program](../java/sample/ScoreMNIST.java) that runs inference on a pretrained MNIST model.

## Running on a GPU or with another provider (Optional)
To enable other execution providers like GPUs simply turn on the appropriate flag on SessionOptions when creating an OrtSession.

    int gpuDeviceId = 0; // The GPU device ID to execute on
    var sessionOptions = new OrtSession.SessionOptions();
    sessionOptions.addCUDA(gpuDeviceId);
    var session = environment.createSession("model.onnx", sessionOptions);

The execution providers are preferred in the order they were enabled.

## API Reference

The Javadoc is available [here](https://microsoft.github.io/onnxruntime/java/index.html).